{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from recognize_computer_vision_linear_model import generate_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "image_data,image_label = generate_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[tensor([[0, 0, 1, 1, 0, 0],\n",
      "        [0, 1, 0, 0, 1, 0],\n",
      "        [0, 1, 0, 0, 1, 0],\n",
      "        [0, 1, 0, 0, 1, 0],\n",
      "        [0, 0, 1, 1, 0, 0],\n",
      "        [0, 0, 0, 0, 0, 0]]), tensor([[0, 0, 0, 1, 0, 0],\n",
      "        [0, 0, 1, 1, 0, 0],\n",
      "        [0, 0, 0, 1, 0, 0],\n",
      "        [0, 0, 0, 1, 0, 0],\n",
      "        [0, 0, 1, 1, 1, 0],\n",
      "        [0, 0, 0, 0, 0, 0]]), tensor([[0, 0, 1, 1, 0, 0],\n",
      "        [0, 1, 0, 0, 1, 0],\n",
      "        [0, 0, 0, 1, 0, 0],\n",
      "        [0, 0, 1, 0, 0, 0],\n",
      "        [0, 1, 1, 1, 1, 0],\n",
      "        [0, 0, 0, 0, 0, 0]]), tensor([[0, 0, 1, 1, 0, 0],\n",
      "        [0, 0, 0, 0, 1, 0],\n",
      "        [0, 0, 1, 1, 0, 0],\n",
      "        [0, 0, 0, 0, 1, 0],\n",
      "        [0, 0, 1, 1, 0, 0],\n",
      "        [0, 0, 0, 0, 0, 0]]), tensor([[0, 0, 0, 0, 1, 0],\n",
      "        [0, 0, 0, 1, 1, 0],\n",
      "        [0, 0, 1, 0, 1, 0],\n",
      "        [0, 1, 1, 1, 1, 1],\n",
      "        [0, 0, 0, 0, 1, 0],\n",
      "        [0, 0, 0, 0, 0, 0]]), tensor([[0, 1, 1, 1, 0, 0],\n",
      "        [0, 1, 0, 0, 0, 0],\n",
      "        [0, 1, 1, 1, 0, 0],\n",
      "        [0, 0, 0, 0, 1, 0],\n",
      "        [0, 1, 1, 1, 0, 0],\n",
      "        [0, 0, 0, 0, 0, 0]]), tensor([[0, 0, 1, 1, 0, 0],\n",
      "        [0, 1, 0, 0, 0, 0],\n",
      "        [0, 1, 1, 1, 0, 0],\n",
      "        [0, 1, 0, 0, 1, 0],\n",
      "        [0, 0, 1, 1, 0, 0],\n",
      "        [0, 0, 0, 0, 0, 0]]), tensor([[0, 1, 1, 1, 1, 0],\n",
      "        [0, 0, 0, 0, 1, 0],\n",
      "        [0, 0, 0, 1, 0, 0],\n",
      "        [0, 0, 0, 1, 0, 0],\n",
      "        [0, 0, 0, 1, 0, 0],\n",
      "        [0, 0, 0, 0, 0, 0]]), tensor([[0, 0, 1, 1, 0, 0],\n",
      "        [0, 1, 0, 0, 1, 0],\n",
      "        [0, 0, 1, 1, 0, 0],\n",
      "        [0, 1, 0, 0, 1, 0],\n",
      "        [0, 0, 1, 1, 0, 0],\n",
      "        [0, 0, 0, 0, 0, 0]]), tensor([[0, 0, 1, 1, 1, 0],\n",
      "        [0, 1, 0, 0, 1, 0],\n",
      "        [0, 0, 1, 1, 1, 0],\n",
      "        [0, 1, 0, 0, 1, 0],\n",
      "        [0, 0, 0, 0, 1, 0],\n",
      "        [0, 0, 0, 0, 0, 0]])]\n"
     ]
    }
   ],
   "source": [
    "print(image_data)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [default]",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
